Overview

Dataset statistics

Number of variables23
Number of observations7697
Missing cells13107
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory699.2 KiB
Average record size in memory93.0 B

Variable types

Numeric17
Categorical6

Warnings

df_index is highly correlated with SampleIDHigh correlation
SampleID is highly correlated with df_indexHigh correlation
incident_diabetes is highly correlated with diabetes_timeHigh correlation
diabetes_time is highly correlated with incident_diabetesHigh correlation
SBP is highly correlated with DBPHigh correlation
DBP is highly correlated with SBPHigh correlation
fasting_glucose is highly correlated with HbA1cHigh correlation
HbA1c is highly correlated with fasting_glucoseHigh correlation
healthy_vegetables is highly correlated with total_fiberHigh correlation
total_fiber is highly correlated with healthy_vegetablesHigh correlation
df_index is highly correlated with SampleIDHigh correlation
SampleID is highly correlated with df_indexHigh correlation
age is highly correlated with junk_foodHigh correlation
BMI is highly correlated with fasting_insulinHigh correlation
SBP is highly correlated with DBPHigh correlation
DBP is highly correlated with SBPHigh correlation
fasting_insulin is highly correlated with BMIHigh correlation
healthy_vegetables is highly correlated with total_fiberHigh correlation
junk_food is highly correlated with ageHigh correlation
total_fiber is highly correlated with healthy_vegetablesHigh correlation
df_index is highly correlated with SampleID and 4 other fieldsHigh correlation
SampleID is highly correlated with df_index and 4 other fieldsHigh correlation
incident_diabetes is highly correlated with df_index and 4 other fieldsHigh correlation
diabetes_time is highly correlated with male and 3 other fieldsHigh correlation
age is highly correlated with current_smoker and 1 other fieldsHigh correlation
male is highly correlated with diabetes_time and 3 other fieldsHigh correlation
BMI is highly correlated with current_smoker and 1 other fieldsHigh correlation
HDL is highly correlated with hypertension and 2 other fieldsHigh correlation
LDL is highly correlated with hypertension and 2 other fieldsHigh correlation
trig is highly correlated with hypertension and 2 other fieldsHigh correlation
SBP is highly correlated with current_smoker and 1 other fieldsHigh correlation
DBP is highly correlated with current_smoker and 1 other fieldsHigh correlation
hypertension is highly correlated with df_index and 9 other fieldsHigh correlation
fasting is highly correlated with current_smoker and 1 other fieldsHigh correlation
fasting_glucose is highly correlated with current_smoker and 1 other fieldsHigh correlation
fasting_insulin is highly correlated with current_smoker and 2 other fieldsHigh correlation
HbA1c is highly correlated with current_smoker and 1 other fieldsHigh correlation
current_smoker is highly correlated with df_index and 17 other fieldsHigh correlation
ex_smoker is highly correlated with df_index and 17 other fieldsHigh correlation
exercise is highly correlated with fasting_insulinHigh correlation
healthy_vegetables is highly correlated with total_fiberHigh correlation
total_fiber is highly correlated with healthy_vegetablesHigh correlation
incident_diabetes is highly correlated with diabetes_timeHigh correlation
DBP is highly correlated with SBPHigh correlation
total_fiber is highly correlated with healthy_vegetablesHigh correlation
diabetes_time is highly correlated with incident_diabetes and 1 other fieldsHigh correlation
df_index is highly correlated with SampleIDHigh correlation
SBP is highly correlated with DBP and 1 other fieldsHigh correlation
HbA1c is highly correlated with fasting_glucoseHigh correlation
SampleID is highly correlated with df_indexHigh correlation
healthy_vegetables is highly correlated with total_fiberHigh correlation
hypertension is highly correlated with SBPHigh correlation
fasting_glucose is highly correlated with diabetes_time and 1 other fieldsHigh correlation
fasting_glucose has 4421 (57.4%) missing values Missing
fasting_insulin has 4421 (57.4%) missing values Missing
HbA1c has 3348 (43.5%) missing values Missing
exercise has 112 (1.5%) missing values Missing
healthy_vegetables has 168 (2.2%) missing values Missing
junk_food has 136 (1.8%) missing values Missing
total_fiber has 419 (5.4%) missing values Missing
df_index is uniformly distributed Uniform
SampleID is uniformly distributed Uniform
df_index has unique values Unique
SampleID has unique values Unique

Reproduction

Analysis started2021-08-13 20:10:22.380968
Analysis finished2021-08-13 20:11:06.211420
Duration43.83 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4148.146421
Minimum1
Maximum8290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2021-08-13T16:11:06.358223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile423.8
Q12076
median4147
Q36217
95-th percentile7878.2
Maximum8290
Range8289
Interquartile range (IQR)4141

Descriptive statistics

Standard deviation2390.894749
Coefficient of variation (CV)0.5763766527
Kurtosis-1.196031006
Mean4148.146421
Median Absolute Deviation (MAD)2071
Skewness0.001183643904
Sum31928283
Variance5716377.7
MonotonicityStrictly increasing
2021-08-13T16:11:06.490438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
54311
 
< 0.1%
55271
 
< 0.1%
55261
 
< 0.1%
55251
 
< 0.1%
55241
 
< 0.1%
55221
 
< 0.1%
55211
 
< 0.1%
55201
 
< 0.1%
55191
 
< 0.1%
Other values (7687)7687
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
82901
< 0.1%
82891
< 0.1%
82881
< 0.1%
82871
< 0.1%
82861
< 0.1%
82851
< 0.1%
82841
< 0.1%
82831
< 0.1%
82821
< 0.1%
82811
< 0.1%

SampleID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4149.146421
Minimum2
Maximum8291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:06.627119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile424.8
Q12077
median4148
Q36218
95-th percentile7879.2
Maximum8291
Range8289
Interquartile range (IQR)4141

Descriptive statistics

Standard deviation2390.894749
Coefficient of variation (CV)0.5762377382
Kurtosis-1.196031006
Mean4149.146421
Median Absolute Deviation (MAD)2071
Skewness0.001183643904
Sum31935980
Variance5716377.7
MonotonicityStrictly increasing
2021-08-13T16:11:06.756796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
54321
 
< 0.1%
55281
 
< 0.1%
55271
 
< 0.1%
55261
 
< 0.1%
55251
 
< 0.1%
55231
 
< 0.1%
55221
 
< 0.1%
55211
 
< 0.1%
55201
 
< 0.1%
Other values (7687)7687
99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
82911
< 0.1%
82901
< 0.1%
82891
< 0.1%
82881
< 0.1%
82871
< 0.1%
82861
< 0.1%
82851
< 0.1%
82841
< 0.1%
82831
< 0.1%
82821
< 0.1%

incident_diabetes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.1 KiB
0
6993 
1
704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7697
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

Length

2021-08-13T16:11:06.989884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:07.057373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

Most occurring characters

ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7697
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common7697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06993
90.9%
1704
 
9.1%

diabetes_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct866
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.73929214
Minimum0.02999999933
Maximum14.96000004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:07.145277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.02999999933
5-th percentile6.079999924
Q114.76000023
median14.81999969
Q314.88000011
95-th percentile14.93000031
Maximum14.96000004
Range14.93000004
Interquartile range (IQR)0.1199998856

Descriptive statistics

Standard deviation2.935263634
Coefficient of variation (CV)0.2136400938
Kurtosis7.112100601
Mean13.73929214
Median Absolute Deviation (MAD)0.06000041962
Skewness-2.81443119
Sum105751.3281
Variance8.615773201
MonotonicityNot monotonic
2021-08-13T16:11:07.292317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.81999969515
 
6.7%
14.88000011492
 
6.4%
14.80000019476
 
6.2%
14.84000015450
 
5.8%
14.92000008409
 
5.3%
14.89999962391
 
5.1%
14.85999966361
 
4.7%
14.93999958281
 
3.7%
14.93000031272
 
3.5%
14.77999973262
 
3.4%
Other values (856)3788
49.2%
ValueCountFrequency (%)
0.029999999331
< 0.1%
0.079999998211
< 0.1%
0.14000000061
< 0.1%
0.1500000062
< 0.1%
0.17000000181
< 0.1%
0.20999999341
< 0.1%
0.251
< 0.1%
0.33000001312
< 0.1%
0.4000000062
< 0.1%
0.41999998692
< 0.1%
ValueCountFrequency (%)
14.960000042
 
< 0.1%
14.93999958281
3.7%
14.93000031272
3.5%
14.92000008409
5.3%
14.90999985262
3.4%
14.89999962391
5.1%
14.89000034223
2.9%
14.88000011492
6.4%
14.86999989251
3.3%
14.85999966361
4.7%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.59828505
Minimum24
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:07.424015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile27
Q137
median48
Q358
95-th percentile69
Maximum74
Range50
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.13774328
Coefficient of variation (CV)0.2760129544
Kurtosis-1.008195018
Mean47.59828505
Median Absolute Deviation (MAD)10
Skewness0.02104560191
Sum366364
Variance172.6002985
MonotonicityNot monotonic
2021-08-13T16:11:07.956865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56256
 
3.3%
55243
 
3.2%
54203
 
2.6%
43195
 
2.5%
42193
 
2.5%
39188
 
2.4%
52187
 
2.4%
37187
 
2.4%
50186
 
2.4%
44182
 
2.4%
Other values (41)5677
73.8%
ValueCountFrequency (%)
2470
0.9%
25142
1.8%
26167
2.2%
27139
1.8%
28159
2.1%
29131
1.7%
30149
1.9%
31146
1.9%
32157
2.0%
33165
2.1%
ValueCountFrequency (%)
7438
 
0.5%
7373
0.9%
7291
1.2%
7178
1.0%
7070
0.9%
6991
1.2%
68100
1.3%
67110
1.4%
66101
1.3%
6585
1.1%

male
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.1 KiB
0
4092 
1
3605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7697
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04092
53.2%
13605
46.8%

Length

2021-08-13T16:11:08.176263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:08.242451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
04092
53.2%
13605
46.8%

Most occurring characters

ValueCountFrequency (%)
04092
53.2%
13605
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7697
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04092
53.2%
13605
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common7697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04092
53.2%
13605
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04092
53.2%
13605
46.8%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7548
Distinct (%)98.1%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean26.72398376
Minimum15.83675671
Maximum55.97962952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:08.329080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum15.83675671
5-th percentile20.50849838
Q123.5651207
median26.17281914
Q329.18943024
95-th percentile35.03690567
Maximum55.97962952
Range40.14287281
Interquartile range (IQR)5.62430954

Descriptive statistics

Standard deviation4.526644707
Coefficient of variation (CV)0.1693851054
Kurtosis1.69921124
Mean26.72398376
Median Absolute Deviation (MAD)2.766759872
Skewness0.9362901449
Sum205587.6094
Variance20.49051285
MonotonicityNot monotonic
2021-08-13T16:11:08.467082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.034610753
 
< 0.1%
21.516910553
 
< 0.1%
28.35992052
 
< 0.1%
20.758440022
 
< 0.1%
24.486059192
 
< 0.1%
24.460300452
 
< 0.1%
22.684310912
 
< 0.1%
21.775960922
 
< 0.1%
28.709829332
 
< 0.1%
24.141269682
 
< 0.1%
Other values (7538)7671
99.7%
(Missing)4
 
0.1%
ValueCountFrequency (%)
15.836756711
< 0.1%
16.374464041
< 0.1%
16.782907491
< 0.1%
16.817230221
< 0.1%
16.890150071
< 0.1%
16.974609381
< 0.1%
16.976921081
< 0.1%
17.018468861
< 0.1%
17.151859281
< 0.1%
17.236070631
< 0.1%
ValueCountFrequency (%)
55.979629521
< 0.1%
53.34746171
< 0.1%
52.010601041
< 0.1%
51.044811251
< 0.1%
48.860343931
< 0.1%
48.59611131
< 0.1%
48.421192171
< 0.1%
48.373538971
< 0.1%
48.092800141
< 0.1%
46.775688171
< 0.1%

HDL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct261
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.510970473
Minimum0.3100000024
Maximum4.199999809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:08.615207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.3100000024
5-th percentile0.9399999976
Q11.210000038
median1.460000038
Q31.75
95-th percentile2.289999962
Maximum4.199999809
Range3.889999807
Interquartile range (IQR)0.5399999619

Descriptive statistics

Standard deviation0.4159130454
Coefficient of variation (CV)0.2752622068
Kurtosis1.220328689
Mean1.510970473
Median Absolute Deviation (MAD)0.2599999905
Skewness0.830974102
Sum11629.93945
Variance0.1729836613
MonotonicityNot monotonic
2021-08-13T16:11:08.817056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.29999995295
 
1.2%
1.4900000191
 
1.2%
1.40999996788
 
1.1%
1.39999997685
 
1.1%
1.54999995285
 
1.1%
1.19000005784
 
1.1%
1.17999994884
 
1.1%
1.2400000184
 
1.1%
1.37000000583
 
1.1%
1.37999999583
 
1.1%
Other values (251)6835
88.8%
ValueCountFrequency (%)
0.31000000241
< 0.1%
0.46999999881
< 0.1%
0.51
< 0.1%
0.51999998092
< 0.1%
0.52999997141
< 0.1%
0.58999997382
< 0.1%
0.60000002382
< 0.1%
0.62000000481
< 0.1%
0.63999998572
< 0.1%
0.64999997622
< 0.1%
ValueCountFrequency (%)
4.1999998091
< 0.1%
3.7699999811
< 0.1%
3.759999991
< 0.1%
3.7200000291
< 0.1%
3.6099998951
< 0.1%
3.51
< 0.1%
3.4200000761
< 0.1%
3.4100000861
< 0.1%
3.1600000862
< 0.1%
3.1500000951
< 0.1%

LDL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct513
Distinct (%)6.7%
Missing14
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.356531382
Minimum0.7699999809
Maximum9.550000191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:08.989086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.7699999809
5-th percentile2
Q12.720000029
median3.299999952
Q33.910000086
95-th percentile4.929999828
Maximum9.550000191
Range8.78000021
Interquartile range (IQR)1.190000057

Descriptive statistics

Standard deviation0.9024052024
Coefficient of variation (CV)0.2688505054
Kurtosis0.8598174453
Mean3.356531382
Median Absolute Deviation (MAD)0.5900001526
Skewness0.5335271358
Sum25788.23047
Variance0.8143351078
MonotonicityNot monotonic
2021-08-13T16:11:09.124546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.79999995249
 
0.6%
3.58999991447
 
0.6%
3.08999991445
 
0.6%
2.90000009543
 
0.6%
3.33999991443
 
0.6%
3.2400000143
 
0.6%
3.40000009542
 
0.5%
2.69000005742
 
0.5%
3.43000006742
 
0.5%
3.29999995242
 
0.5%
Other values (503)7245
94.1%
ValueCountFrequency (%)
0.76999998091
< 0.1%
0.80000001191
< 0.1%
0.83999997381
< 0.1%
0.86000001431
< 0.1%
11
< 0.1%
1.009999991
< 0.1%
1.0299999711
< 0.1%
1.0399999621
< 0.1%
1.0599999431
< 0.1%
1.0800000431
< 0.1%
ValueCountFrequency (%)
9.5500001911
< 0.1%
8.939999581
< 0.1%
7.8400001531
< 0.1%
7.7600002291
< 0.1%
7.4400000571
< 0.1%
7.4000000951
< 0.1%
7.1399998662
< 0.1%
6.9400000571
< 0.1%
6.8899998661
< 0.1%
6.8299999241
< 0.1%

trig
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.407413244
Minimum0.2700000107
Maximum18.45999908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:09.272060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2700000107
5-th percentile0.5899999738
Q10.8399999738
median1.159999967
Q31.659999967
95-th percentile3.019999981
Maximum18.45999908
Range18.18999907
Interquartile range (IQR)0.8199999928

Descriptive statistics

Standard deviation0.9503949881
Coefficient of variation (CV)0.6752778292
Kurtosis44.55989075
Mean1.407413244
Median Absolute Deviation (MAD)0.3699999452
Skewness4.537890911
Sum10832.85938
Variance0.9032506347
MonotonicityNot monotonic
2021-08-13T16:11:09.415664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.829999983385
 
1.1%
0.879999995276
 
1.0%
1.0099999975
 
1.0%
0.819999992873
 
0.9%
0.759999990573
 
0.9%
0.980000019172
 
0.9%
0.810000002471
 
0.9%
0.920000016770
 
0.9%
0.790000021570
 
0.9%
0.800000011969
 
0.9%
Other values (459)6963
90.5%
ValueCountFrequency (%)
0.27000001072
 
< 0.1%
0.30000001191
 
< 0.1%
0.31000000241
 
< 0.1%
0.31999999281
 
< 0.1%
0.33000001311
 
< 0.1%
0.34000000361
 
< 0.1%
0.36000001435
0.1%
0.37000000482
 
< 0.1%
0.37999999525
0.1%
0.38999998578
0.1%
ValueCountFrequency (%)
18.459999081
< 0.1%
17.239999771
< 0.1%
14.159999851
< 0.1%
12.751
< 0.1%
11.739999771
< 0.1%
111
< 0.1%
10.670000081
< 0.1%
10.51
< 0.1%
10.210000041
< 0.1%
9.9799995421
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct134
Distinct (%)1.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean134.715683
Minimum89
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:09.574226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile108
Q1120
median132
Q3146
95-th percentile171
Maximum228
Range139
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.81209183
Coefficient of variation (CV)0.1470659673
Kurtosis0.7745363712
Mean134.715683
Median Absolute Deviation (MAD)13
Skewness0.8007217646
Sum1036502.5
Variance392.5190125
MonotonicityNot monotonic
2021-08-13T16:11:09.722127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124184
 
2.4%
127181
 
2.4%
126177
 
2.3%
121177
 
2.3%
129174
 
2.3%
128174
 
2.3%
125174
 
2.3%
130174
 
2.3%
123173
 
2.2%
131172
 
2.2%
Other values (124)5934
77.1%
ValueCountFrequency (%)
891
 
< 0.1%
912
 
< 0.1%
922
 
< 0.1%
932
 
< 0.1%
943
 
< 0.1%
954
 
0.1%
9611
0.1%
978
0.1%
9811
0.1%
9915
0.2%
ValueCountFrequency (%)
2281
 
< 0.1%
2272
< 0.1%
2261
 
< 0.1%
2191
 
< 0.1%
2181
 
< 0.1%
2171
 
< 0.1%
2151
 
< 0.1%
2143
< 0.1%
2131
 
< 0.1%
2121
 
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct83
Distinct (%)1.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean79.01624298
Minimum39
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:09.875080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile61
Q171
median79
Q387
95-th percentile98
Maximum126
Range87
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.33845806
Coefficient of variation (CV)0.1434952766
Kurtosis0.1288583875
Mean79.01624298
Median Absolute Deviation (MAD)8
Skewness0.1495639384
Sum607951
Variance128.5606384
MonotonicityNot monotonic
2021-08-13T16:11:10.020990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77305
 
4.0%
79293
 
3.8%
83268
 
3.5%
81265
 
3.4%
80263
 
3.4%
78263
 
3.4%
74260
 
3.4%
75256
 
3.3%
85247
 
3.2%
76245
 
3.2%
Other values (73)5029
65.3%
ValueCountFrequency (%)
391
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
452
 
< 0.1%
465
0.1%
471
 
< 0.1%
484
0.1%
498
0.1%
505
0.1%
ValueCountFrequency (%)
1261
 
< 0.1%
1242
< 0.1%
1231
 
< 0.1%
1221
 
< 0.1%
1202
< 0.1%
1191
 
< 0.1%
1173
< 0.1%
1164
0.1%
1154
0.1%
1144
0.1%

hypertension
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size451.1 KiB
0.0
5837 
1.0
1860 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23091
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05837
75.8%
1.01860
 
24.2%

Length

2021-08-13T16:11:10.262415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:10.333477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05837
75.8%
1.01860
 
24.2%

Most occurring characters

ValueCountFrequency (%)
013534
58.6%
.7697
33.3%
11860
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15394
66.7%
Other Punctuation7697
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013534
87.9%
11860
 
12.1%
Other Punctuation
ValueCountFrequency (%)
.7697
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common23091
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013534
58.6%
.7697
33.3%
11860
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII23091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013534
58.6%
.7697
33.3%
11860
 
8.1%

fasting
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)0.4%
Missing20
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.845252037
Minimum0
Maximum31
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:10.402634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile14
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.108305454
Coefficient of variation (CV)0.5317658782
Kurtosis6.96020937
Mean5.845252037
Median Absolute Deviation (MAD)1
Skewness2.49319315
Sum44874
Variance9.66156292
MonotonicityNot monotonic
2021-08-13T16:11:10.509561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
52756
35.8%
41709
22.2%
61365
17.7%
7450
 
5.8%
3238
 
3.1%
2189
 
2.5%
8160
 
2.1%
15112
 
1.5%
14110
 
1.4%
1699
 
1.3%
Other values (17)489
 
6.4%
ValueCountFrequency (%)
04
 
0.1%
171
 
0.9%
2189
 
2.5%
3238
 
3.1%
41709
22.2%
52756
35.8%
61365
17.7%
7450
 
5.8%
8160
 
2.1%
966
 
0.9%
ValueCountFrequency (%)
311
 
< 0.1%
282
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
223
 
< 0.1%
213
 
< 0.1%
2014
 
0.2%
1913
 
0.2%
1848
0.6%
1759
0.8%

fasting_glucose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct362
Distinct (%)11.1%
Missing4421
Missing (%)57.4%
Infinite0
Infinite (%)0.0%
Mean5.829258442
Minimum4.159999847
Maximum18.81999969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:10.644989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.159999847
5-th percentile4.900000095
Q15.360000134
median5.71999979
Q36.130000114
95-th percentile6.960000038
Maximum18.81999969
Range14.65999985
Interquartile range (IQR)0.7699999809

Descriptive statistics

Standard deviation0.8572081923
Coefficient of variation (CV)0.1470527053
Kurtosis58.63638687
Mean5.829258442
Median Absolute Deviation (MAD)0.3799996376
Skewness5.31879282
Sum19096.65039
Variance0.734805882
MonotonicityNot monotonic
2021-08-13T16:11:10.781217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.73999977135
 
0.5%
5.38000011434
 
0.4%
5.48000001933
 
0.4%
5.61999988629
 
0.4%
5.7199997929
 
0.4%
5.82000017229
 
0.4%
5.5300002129
 
0.4%
5.63000011428
 
0.4%
5.59000015328
 
0.4%
5.61000013427
 
0.4%
Other values (352)2975
38.7%
(Missing)4421
57.4%
ValueCountFrequency (%)
4.1599998471
 
< 0.1%
4.2600002291
 
< 0.1%
4.2699999811
 
< 0.1%
4.3200001721
 
< 0.1%
4.3299999241
 
< 0.1%
4.3899998661
 
< 0.1%
4.4000000951
 
< 0.1%
4.4099998471
 
< 0.1%
4.4400000573
< 0.1%
4.4499998091
 
< 0.1%
ValueCountFrequency (%)
18.819999691
< 0.1%
17.139999391
< 0.1%
16.870000841
< 0.1%
16.709999081
< 0.1%
16.260000231
< 0.1%
14.640000341
< 0.1%
13.869999891
< 0.1%
13.420000081
< 0.1%
12.939999581
< 0.1%
12.710000041
< 0.1%

fasting_insulin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct288
Distinct (%)8.8%
Missing4421
Missing (%)57.4%
Infinite0
Infinite (%)0.0%
Mean8.934035301
Minimum0.8000000119
Maximum117.0999985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:11.015959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.8000000119
5-th percentile3
Q15.099999905
median7.300000191
Q310.80000019
95-th percentile19.79999924
Maximum117.0999985
Range116.2999985
Interquartile range (IQR)5.700000286

Descriptive statistics

Standard deviation6.581742764
Coefficient of variation (CV)0.7367043495
Kurtosis42.87613678
Mean8.934035301
Median Absolute Deviation (MAD)2.600000381
Skewness4.44527483
Sum29267.90039
Variance43.31933594
MonotonicityNot monotonic
2021-08-13T16:11:11.221664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.19999980948
 
0.6%
5.30000019146
 
0.6%
6.90000009545
 
0.6%
5.09999990544
 
0.6%
643
 
0.6%
5.80000019141
 
0.5%
4.541
 
0.5%
740
 
0.5%
539
 
0.5%
7.539
 
0.5%
Other values (278)2850
37.0%
(Missing)4421
57.4%
ValueCountFrequency (%)
0.80000001192
 
< 0.1%
11
 
< 0.1%
1.2000000482
 
< 0.1%
1.2999999522
 
< 0.1%
1.3999999761
 
< 0.1%
1.54
 
0.1%
1.6000000247
0.1%
1.7000000485
0.1%
1.7999999526
0.1%
1.89999997610
0.1%
ValueCountFrequency (%)
117.09999851
< 0.1%
91.699996951
< 0.1%
75.199996951
< 0.1%
71.699996951
< 0.1%
671
< 0.1%
56.200000761
< 0.1%
55.900001531
< 0.1%
551
< 0.1%
531
< 0.1%
52.799999241
< 0.1%

HbA1c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)1.0%
Missing3348
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean36.00069046
Minimum20
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:11.359394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q133
median36
Q338
95-th percentile42
Maximum142
Range122
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.656335831
Coefficient of variation (CV)0.1293401867
Kurtosis86.22382355
Mean36.00069046
Median Absolute Deviation (MAD)3
Skewness4.902283192
Sum156567
Variance21.68146324
MonotonicityNot monotonic
2021-08-13T16:11:11.531155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36565
 
7.3%
34550
 
7.1%
37538
 
7.0%
38451
 
5.9%
33442
 
5.7%
39319
 
4.1%
32312
 
4.1%
40253
 
3.3%
31213
 
2.8%
41139
 
1.8%
Other values (33)567
 
7.4%
(Missing)3348
43.5%
ValueCountFrequency (%)
201
 
< 0.1%
211
 
< 0.1%
231
 
< 0.1%
254
 
0.1%
2619
 
0.2%
2735
 
0.5%
2842
 
0.5%
2968
 
0.9%
30114
1.5%
31213
2.8%
ValueCountFrequency (%)
1421
< 0.1%
1051
< 0.1%
1031
< 0.1%
791
< 0.1%
741
< 0.1%
701
< 0.1%
641
< 0.1%
621
< 0.1%
581
< 0.1%
572
< 0.1%

current_smoker
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing38
Missing (%)0.5%
Memory size450.4 KiB
0.0
5647 
1.0
2012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22977
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05647
73.4%
1.02012
 
26.1%
(Missing)38
 
0.5%

Length

2021-08-13T16:11:11.765385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:11.830026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05647
73.7%
1.02012
 
26.3%

Most occurring characters

ValueCountFrequency (%)
013306
57.9%
.7659
33.3%
12012
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15318
66.7%
Other Punctuation7659
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013306
86.9%
12012
 
13.1%
Other Punctuation
ValueCountFrequency (%)
.7659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013306
57.9%
.7659
33.3%
12012
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII22977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013306
57.9%
.7659
33.3%
12012
 
8.8%

ex_smoker
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size436.1 KiB
0
6058 
1
1639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7697
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

Length

2021-08-13T16:11:12.017685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:12.088274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

Most occurring characters

ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7697
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common7697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06058
78.7%
11639
 
21.3%

exercise
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing112
Missing (%)1.5%
Memory size448.9 KiB
2.0
4100 
3.0
1702 
1.0
1692 
4.0
 
91

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22755
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.04100
53.3%
3.01702
22.1%
1.01692
22.0%
4.091
 
1.2%
(Missing)112
 
1.5%

Length

2021-08-13T16:11:12.271365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-13T16:11:12.337866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.04100
54.1%
3.01702
22.4%
1.01692
22.3%
4.091
 
1.2%

Most occurring characters

ValueCountFrequency (%)
.7585
33.3%
07585
33.3%
24100
18.0%
31702
 
7.5%
11692
 
7.4%
491
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15170
66.7%
Other Punctuation7585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07585
50.0%
24100
27.0%
31702
 
11.2%
11692
 
11.2%
491
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.7585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.7585
33.3%
07585
33.3%
24100
18.0%
31702
 
7.5%
11692
 
7.4%
491
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII22755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.7585
33.3%
07585
33.3%
24100
18.0%
31702
 
7.5%
11692
 
7.4%
491
 
0.4%

healthy_vegetables
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)0.2%
Missing168
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean10.72174263
Minimum3
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:12.410874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q18
median11
Q313
95-th percentile16
Maximum18
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.412720203
Coefficient of variation (CV)0.3182990253
Kurtosis-0.5452287793
Mean10.72174263
Median Absolute Deviation (MAD)2
Skewness-0.119201526
Sum80724
Variance11.64665985
MonotonicityNot monotonic
2021-08-13T16:11:12.507720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11835
10.8%
10808
10.5%
12798
10.4%
13744
9.7%
9688
8.9%
8597
7.8%
14596
7.7%
15478
6.2%
7450
 
5.8%
6369
 
4.8%
Other values (6)1166
15.1%
ValueCountFrequency (%)
3118
 
1.5%
4192
 
2.5%
5261
 
3.4%
6369
4.8%
7450
5.8%
8597
7.8%
9688
8.9%
10808
10.5%
11835
10.8%
12798
10.4%
ValueCountFrequency (%)
18143
 
1.9%
17132
 
1.7%
16320
 
4.2%
15478
6.2%
14596
7.7%
13744
9.7%
12798
10.4%
11835
10.8%
10808
10.5%
9688
8.9%

junk_food
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct20
Distinct (%)0.3%
Missing136
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean8.333156586
Minimum5
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:12.615372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median8
Q310
95-th percentile14
Maximum24
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.023710728
Coefficient of variation (CV)0.3628529906
Kurtosis1.059070587
Mean8.333156586
Median Absolute Deviation (MAD)2
Skewness1.053599954
Sum63007
Variance9.142827034
MonotonicityNot monotonic
2021-08-13T16:11:12.723554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
51461
19.0%
61227
15.9%
7885
11.5%
8865
11.2%
9829
10.8%
10657
8.5%
11509
 
6.6%
12364
 
4.7%
13272
 
3.5%
14170
 
2.2%
Other values (10)322
 
4.2%
(Missing)136
 
1.8%
ValueCountFrequency (%)
51461
19.0%
61227
15.9%
7885
11.5%
8865
11.2%
9829
10.8%
10657
8.5%
11509
 
6.6%
12364
 
4.7%
13272
 
3.5%
14170
 
2.2%
ValueCountFrequency (%)
243
 
< 0.1%
231
 
< 0.1%
224
 
0.1%
212
 
< 0.1%
2013
 
0.2%
1916
 
0.2%
1829
 
0.4%
1758
0.8%
1674
1.0%
15122
1.6%

total_fiber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)0.5%
Missing419
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean31.862463
Minimum9
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2021-08-13T16:11:12.883483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile21
Q128
median32
Q336
95-th percentile41
Maximum48
Range39
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.202386856
Coefficient of variation (CV)0.1946612448
Kurtosis-0.07558012009
Mean31.862463
Median Absolute Deviation (MAD)4
Skewness-0.3200424314
Sum231895
Variance38.46960449
MonotonicityNot monotonic
2021-08-13T16:11:13.051992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
33487
 
6.3%
34475
 
6.2%
35452
 
5.9%
30440
 
5.7%
31439
 
5.7%
32427
 
5.5%
36416
 
5.4%
37361
 
4.7%
28360
 
4.7%
29360
 
4.7%
Other values (30)3061
39.8%
(Missing)419
 
5.4%
ValueCountFrequency (%)
91
 
< 0.1%
102
 
< 0.1%
112
 
< 0.1%
129
 
0.1%
137
 
0.1%
1418
0.2%
1520
0.3%
1621
0.3%
1742
0.5%
1841
0.5%
ValueCountFrequency (%)
483
 
< 0.1%
4711
 
0.1%
4623
 
0.3%
4538
 
0.5%
4466
 
0.9%
4386
 
1.1%
42132
1.7%
41153
2.0%
40211
2.7%
39304
3.9%

Interactions

2021-08-13T16:10:33.819865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:33.960529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.074233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.184259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.288182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.408827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.521812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.625178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.741737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.850264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:34.962938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.076536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.282943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.430372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.558550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.753623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.877093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:35.973066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:36.054897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:36.305433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.064664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.220736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.337350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.448954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.540487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.636424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.727292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.808133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.895254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:37.988958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:38.114661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:38.233955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:38.851814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.000431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.127777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.243537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.366321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.497925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.617834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.747386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.863178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:39.969791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.091961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.241880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.350826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.448985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.547200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.643616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.738122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.834235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:40.943240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.053498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.142772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.230538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.326719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.413376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.512017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.783848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.885583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:41.994789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.094445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.183184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.274382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.389874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.498746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.596799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.694498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.786728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.879358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:42.970160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.063634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.176386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.279143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.396029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.511029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.644298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.766016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.887357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:43.997909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.113377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.231765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.337303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.441643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.541075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.654991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.759546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.851938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:44.945331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.040582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.133760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.232129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.328531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.423207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.531832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.628165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.717662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.811347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:45.906104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.001670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.096629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.202571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.301001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.615146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.701276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.780274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.862859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:46.943903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.029009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.111571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.188053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.274642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.354542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.434293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.519245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.600647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.682599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.766594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.848640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:47.934124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.017167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.105126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.204576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.305494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.398219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.494680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.592198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.681759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.778475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.875836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:48.964024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.056839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.153421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.247887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.341493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.440266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.535589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.630984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.715631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.800766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.892258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:49.979303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.076662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.167664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.259268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.359296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.453553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.538834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.631261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.724475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.822098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:50.915710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.009152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.109562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.204963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.288239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.369375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.458507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.537157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.624457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:51.709358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.043761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.136357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.219286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.306230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.398929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.500003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.597687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.687516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.775893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.862719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:52.950900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.035436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.124771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.214423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.309412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.409290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.503798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.589790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.690766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.785947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.873852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:53.964026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.061299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.158265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.248793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.339379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.432694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.523850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.611951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.700665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.806621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:54.911829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.014960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.121837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.211411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.313048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.428845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.542542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.647489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.753653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.853442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:55.952252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.056121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.158444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.258402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.350197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.440002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.533266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.627145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.719450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.810493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:56.911985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.023548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.132974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.232600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.327358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.429057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.523926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.618557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.713942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.812421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:57.914079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.008504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.103471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.213880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.330327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.430480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.529421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:58.615838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.034605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.149895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.248951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.347888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.452069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.545753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.639489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.733768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.827066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:10:59.918274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.014181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.114990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.221239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.339037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.439951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.538724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.627742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.724443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.817783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:00.912200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.007467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.113374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.210744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.323594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.438526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.557158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.655543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.747854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.837584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:01.936172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.026626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.122237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.217529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.305668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.405561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.504926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.599851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.697932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.796288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.895388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:02.998810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.103130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.211217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.312870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.409008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.519723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.631251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.730946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.834256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:03.947656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.039338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.144477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.245294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.336004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.429752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.523556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.612709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.704732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.800051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-13T16:11:04.905078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-13T16:11:13.301945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-13T16:11:13.612369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-13T16:11:13.955510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-13T16:11:14.213634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-13T16:11:14.414639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-13T16:11:05.155588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-13T16:11:05.588839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-13T16:11:05.876249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-13T16:11:06.065925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexSampleIDincident_diabetesdiabetes_timeagemaleBMIHDLLDLtrigSBPDBPhypertensionfastingfasting_glucosefasting_insulinHbA1ccurrent_smokerex_smokerexercisehealthy_vegetablesjunk_foodtotal_fiber
012014.8269043.7840501.603.881.85178.079.00.05.06.9119.60000037.00.003.015.07.041.0
123014.8272123.0359591.552.971.12156.075.01.06.0NaNNaN37.00.003.08.06.032.0
23412.2068039.4216611.202.802.33154.080.00.04.08.8333.400002NaN0.001.013.0NaN35.0
345014.8260027.8966811.702.981.29121.077.00.06.05.868.80000038.00.002.09.08.037.0
456014.8225124.7957191.253.100.84139.078.00.015.0NaNNaN27.00.012.011.06.034.0
567014.8259026.2646201.772.800.83139.064.00.04.06.728.00000034.00.002.016.010.045.0
67816.1259038.2797891.714.012.14192.083.01.015.0NaNNaN39.00.001.011.08.033.0
789014.8238124.2785091.282.631.32118.063.00.04.0NaNNaN33.00.003.09.06.028.0
8910014.8258024.2702601.473.801.52167.076.00.05.0NaNNaN34.00.002.012.09.037.0
91011014.8227031.6799111.353.330.97121.065.00.04.0NaNNaN32.00.002.010.013.035.0

Last rows

df_indexSampleIDincident_diabetesdiabetes_timeagemaleBMIHDLLDLtrigSBPDBPhypertensionfastingfasting_glucosefasting_insulinHbA1ccurrent_smokerex_smokerexercisehealthy_vegetablesjunk_foodtotal_fiber
768782818282014.9427021.6006622.062.391.21123.070.00.05.0NaNNaN34.00.003.013.010.032.0
768882828283014.9446123.6056820.953.263.23118.082.00.06.05.6912.037.00.002.010.015.033.0
76898283828402.9857025.1880991.452.801.41157.087.00.05.06.256.040.00.013.014.05.039.0
76908284828517.0259029.1747091.213.242.53138.082.00.06.06.3710.342.00.002.016.05.044.0
76918285828616.0664123.5242041.242.691.37168.073.01.05.06.8011.046.00.012.06.05.021.0
769282868287014.8935121.6262971.703.021.01129.072.00.04.0NaNNaN33.00.004.08.013.027.0
769382878288010.5469123.8776531.534.871.44125.070.00.07.06.104.837.00.002.011.09.033.0
769482888289014.8944126.0559331.533.610.93131.075.01.04.0NaNNaN36.00.003.07.011.027.0
769582898290014.8930025.0447851.632.110.85123.071.00.05.0NaNNaN37.00.003.010.08.030.0
769682908291014.8927021.7448481.202.441.61134.064.00.02.0NaNNaN32.01.001.011.07.028.0